package com.atguigu.day10;

import com.atguigu.bean.WaterSensor;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple1;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.annotation.DataTypeHint;
import org.apache.flink.table.annotation.FunctionHint;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.functions.ScalarFunction;
import org.apache.flink.table.functions.TableFunction;
import org.apache.flink.types.Row;

import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.call;

public class Flink02_SQL_UDF_TableFun {
    public static void main(String[] args) {
        //1.获取流的执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //2.从端口读取数据并转为WaterSensor
        SingleOutputStreamOperator<WaterSensor> waterSensorStream = env.socketTextStream("localhost", 9999)
                .map(new MapFunction<String, WaterSensor>() {
                    @Override
                    public WaterSensor map(String value) throws Exception {
                        String[] split = value.split(",");
                        return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
                    }
                });

        //3.获取表的执行环境
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        //4.将流转为表
        Table table = tableEnv.fromDataStream(waterSensorStream);

        //5.不注册直接在TableAPI中使用
       /* table.joinLateral(call(MyUDTF.class,$("id")).as("newField"))
                .select($("id"),$("newField"))
                .execute()
                .print();*/

       //先注册然后在TableAPI中使用
        tableEnv.createTemporarySystemFunction("subIdStr", MyUDTF.class);
          /*table.joinLateral(call("subIdStr",$("id")).as("newField"))
                .select($("id"),$("newField"))
                .execute()
                .print();*/

         //在SQL中使用 一定要先注册
        tableEnv.executeSql("select id,f0 from "+table+" join lateral table(subIdStr(id)) on true").print();



    }

    //自定义一个类继承TableFun 通过这个函数，获取将id按照“_”切分获取两条数据
//    @FunctionHint(output = @DataTypeHint("ROW<word STRING>"))
    public static class MyUDTF extends TableFunction<Tuple1<String>>{
        public void eval(String value){
            String[] strings = value.split("_");
            for (String string : strings) {
//                collect(Row.of(string));
                collect(Tuple1.of(string));
            }
        }
    }

}
